On-line wear estimation using neural networks

1998 ◽  
Vol 212 (2) ◽  
pp. 105-112 ◽  
Author(s):  
A Ghasempoor ◽  
T N Moore ◽  
J Jeswiet
Keyword(s):  
Neural Networks ◽  
Tool Wear ◽  
Cutting Force ◽  
Machining Parameters ◽  
Dynamic Neural Networks ◽  
Line Wear ◽  
Cutting Force Components ◽  
On Line ◽  
Force Components ◽  
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In this paper, a neural network based system for ‘on-line’ estimation of tool wear in turning operations is introduced. The system monitors the cutting force components and extracts the tool wear information from the changes occurring over the cutting process. A hierarchical structure using multilayered feedforward static and dynamic neural networks is used as a specialized subsystem, for each wear component to be monitored. These subsystems share information about the tool wear components they are monitoring and their error in estimating the cutting force components is used to update the dynamic neural networks. The adaptability property of neural networks ensures that changes in machining parameters can be accommodated. Simulation studies are undertaken using experimental data available from manufacturing literature. The results are promising and show good estimation ability.

scholarly journals Trochoidal Milling and Neural Networks Simulation of Magnesium Alloys

Materials ◽  
2019 ◽  
Vol 12 (13) ◽  
pp. 2070 ◽  
Author(s):  
Ireneusz Zagórski ◽  
Monika Kulisz ◽  
Mariusz Kłonica ◽  
Jakub Matuszak
Keyword(s):  
Neural Networks ◽  
Cutting Force ◽  
Magnesium Alloys ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Milling Process ◽  
Machining Parameters ◽  
Cutting Force Components ◽  
Multilayered Perceptron ◽  
Force Components

This paper set out to investigate the effect of cutting speed vc and trochoidal step str modification on selected machinability parameters (the cutting force components and vibration). In addition, for a more detailed analysis, selected surface roughness parameters were investigated. The research was carried out for two grades of magnesium alloys—AZ91D and AZ31—and aimed to determine stable machining parameters and to investigate the dynamics of the milling process, i.e., the resulting change in the cutting force components and in vibration. The tests were performed for the specified range of cutting parameters: vc = 400–1200 m/min and str = 5–30%. The results demonstrate a significant effect of cutting data modification on the parameter under scrutiny—the increase in vc resulted in the reduction of the cutting force components and the displacement and level of vibration recorded in tests. Selected cutting parameters were modelled by means of Statistica Artificial Neural Networks (Radial Basis Function and Multilayered Perceptron), which, furthermore, confirmed the suitability of neural networks as a tool for prediction of the cutting force and vibration in milling of magnesium alloys.

Artificial neural networks back propagation algorithm for cutting force components predictions

2013 ◽  
Vol 14 (6) ◽  
pp. 431-439 ◽  
Author(s):  
Issam Hanafi ◽  
Francisco Mata Cabrera ◽  
Abdellatif Khamlichi ◽  
Ignacio Garrido ◽  
José Tejero Manzanares
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Cutting Force ◽  
Back Propagation ◽  
Back Propagation Algorithm ◽  
Propagation Algorithm ◽  
Cutting Force Components ◽  
Artificial Neural ◽  
Force Components

Loading of the Manufacturing Systems Elements in the Process of Unsteady Mode Cutting and the Models of their Arrangement Deviations

2014 ◽  
Vol 682 ◽  
pp. 192-195 ◽  
Author(s):  
U.S. Putilova ◽  
Yu.I. Nekrasov ◽  
A.A. Lasukov
Keyword(s):  
Cutting Force ◽  
Machine Tools ◽  
Manufacturing Systems ◽  
Diagnostic System ◽  
Cnc Machine Tools ◽  
Cnc Machine ◽  
Cutting Force Components ◽  
On Line ◽  
Servo Drives ◽  
Force Components

To improve the treatment accuracy by on-line correction of the paths of the executive working parts (EWP) the authors study the processes of loading, deformation and arrangement deviation of the elements of the manufacturing systems (MS) under the changes of the cutting force components in the process of turning on machine-tools equipped with CNC systems of PCNC class. Estimation of the values of the technological components of the cutting force Px, Py, Pz is based on the phenomenon of arrangement deviation Δωi of the elements monitoring the servo drives of machine tools. To determine the compliance of the deviation magnitude Δwi with the technological components of the cutting force Px, Py a diagnostic subsystem was developed, involving the loading devices and dynamometric equipment. The diagnostic system is controlled through PCNC with the application of a specially developed hardware-software system. The data on changes in the values Px, Py and the respective changes in the attitude misalignment parameters in servo drives at various EWP minute feeds in CNC machine tools were determined by prior diagnosis of load characteristics servo drives, registered in the PCNC. So, the data of cutting force components Px, Py compliance with arrangement error ratios ΔωXп, ΔωZп. were established.

scholarly journals Diagnosis of edge condition based on force measurement during milling of composites

2018 ◽  
Vol 38 (1) ◽  
pp. 8-14 ◽  
Author(s):  
Agata Felusiak ◽  
Paweł Twardowski
Keyword(s):  
Neural Networks ◽  
Tool Wear ◽  
Ceramic Composite ◽  
Cutting Tool ◽  
Force Measurement ◽  
Edge Condition ◽  
Rbf Neural Networks ◽  
Cutting Force Components ◽  
Force Components ◽  
Comparative Results

Abstract The present paper presents comparative results of the forecasting of a cutting tool wear with the application of different methods of diagnostic deduction based on the measurement of cutting force components. The research was carried out during the milling of the Duralcan F3S.10S aluminum-ceramic composite. Prediction of the toolwear was based on one variable, two variables regression Multilayer Perceptron(MLP)and Radial Basis Function(RBF)neural networks. Forecasting the condition of the cutting tool on the basis of cutting forces has yielded very satisfactory results.

A Study on the Relationships Between Static/Dynamic Cutting Force Components and Tool Wear

2000 ◽  
Vol 123 (2) ◽  
pp. 196-205 ◽  
Author(s):  
Jae-Woong Youn ◽  
Min-Yang Yang
Keyword(s):  
Tool Wear ◽  
Cutting Force ◽  
Metal Cutting ◽  
Cutting Conditions ◽  
Dynamic Force ◽  
Crater Wear ◽  
Cutting Force Components ◽  
Force Components ◽  
Dynamic Components ◽  
Process Detection

The development of flexible automation in the manufacturing industry is concerned with production activities performed by unmanned machining systems. A major topic relevant to metal-cutting operations is monitoring tool wear, which affects process efficiency and product quality, and implementing automatic tool replacements. In this paper, the measurement of the cutting force components has been found to provide a method for an in-process detection of tool wear. Cutting force components are divided into static and dynamic components in this paper. The static components of cutting force have been used to detect flank wear and the dynamic components of cutting force have been analyzed to detect crater wear. To eliminate the influence of variations in cutting conditions, tools, and workpiece materials, the relationships between normalized cutting forces and cutting conditions are established. According to the proposed method, the static and dynamic force components could provide the effective means to detect flank and crater wear for varying cutting conditions in turning operation.

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